Behind the Screen: Content Moderation in the Shadows of Social Media

By Ysabel Gerrard. 2019

396 citations recorded by the source corpus.

It would be little overstatement to claim that these dynamics are a huge experiment in seeking to convert vernacular or informal creativity into talent and content increasingly attractive to advertisers, brands, talent agencies, studios, and venture-capital investors on a near global scale – with implications for content/entertainment formats, production cultures, industry structures, and measurement of audience engagement . . . (p. 5)

Subfields: Artificial Intelligence, Communication, AI Ethics, Fairness & Society

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Citations in the corpus

Evaluating the Social Impact of Generative AI Systems in Systems and Society (2023, background): “Since many critical crowdworking jobs and evaluation of this work involves long-term exposure to traumatic content [199], such as child sexual abuse material or graphic depictions of violence [181], it may also be necessary to consider professional support for mental health and practices to limit th”
Challenges in Automated Debiasing for Toxic Language Detection (2021, methodology): “We test the use of debiasing3 methods for the task of toxic language detection, which aims to flag rude, offensive, hateful, or toxic language on the internet, with the goal of moderating online communities (Roberts, 2019; Vidgen et al., 2019).”
Content moderation, AI, and the question of scale (2020, background): “As Roberts (2019) has documented, these moderators are doing psychologically scarring work, in sometimes intolerable conditions, often under pre-carious labor arrangements.”
Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media (2018, background): “For example, Susan Roberts’ (2019) ethnographic study of the commercial content moderation industry and Nicolas P. Suzor’s (2019) research on how our digital environments are governed and censored by tech companies build upon Gillespie’s work.”
Fast, Accurate, and Healthier: Interactive Blurring Helps Moderators Reduce Exposure to Harmful Content (2020, methodology): “Commercial content moderation (CCM) consists of assessing user-generated content (UGC) for compliance with a social media platform’s terms of service and community guidelines (Roberts 2019).”
Identifying Challenges and Opportunities in Human-AI Collaboration in Healthcare (2019, background): “Even for AI systems, people often directly, but invisibly, contribute to making these systems work [11, 17], which Gray and Suri (20019) term “Ghost Work” [10].”
Algorithmic content moderation: Technical and political challenges in the automation of platform governance (2020, background): “…if these moderation systems achieve their overarching aim by becoming an infrastructure that smoothly operates in the background, that is taken for granted, and that obscures its inner workings, becoming hidden, much like the labour and practices of content moderation used to be (Roberts, 2019)?”
The Psychological Well-Being of Content Moderators: The Emotional Labor of Commercial Moderation and Avenues for Improving Support (2021, background): “Given recent litigation [54, 56], Roberts speculates that “...there may be liability for firms and platforms that do not take sufficient measures to shield... workers from damaging content whenever possible and to offer them adequate psychological support when it is not.””
Studying Generalisability across Abusive Language Detection Datasets (2019, background): “Another issue that was highlighted by Roberts (2019) is the impact that reviewing online abuse can have on a worker’s mental well-being.”
Expanding the debate about content moderation: Scholarly research agendas for the coming policy debates (2020, neutral): “, Youtube, Facebook, TikTok, Instagram) use commercial content moderators (Roberts, 2019) to shape this world every day, according to ever-changing guidelines.”